

def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			stooge(a__  ,	0  ,	len(a__) - 1)
			return arr


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			if i >= h:
						return

			# If first element is smaller than the last then swap them
			if arr[i] > arr[h]:
						a_							,					a_						:						Dict 							= arr[h], arr[i]

			# If there are more than 2 elements in the array
			if h - i + 1 > 2:
						a_						:						Dict 							= (int)((h - i + 1) / 3)

						# Recursively sort first 2/3 elements
						stooge(a__  ,	a__  ,	(h - t))

						# Recursively sort last 2/3 elements
						stooge(a__  ,	i + t  ,	(a__))

						# Recursively sort first 2/3 elements
						stooge(a__  ,	a__  ,	(h - t))


if __name__ == "__main__":
	__snake_case				:      Optional[int]   		=			input("""Enter numbers separated by a comma:\n""").strip()
	__snake_case				:      Any   		=			[int(item) for item in user_input.split(""",""")]
	print(stooge_sort(unsorted))





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


